Clean Data
Merritt Harlan
11/21/2020
Tanana
Load raw WSE data
library(readr)
library(dplyr)
library(DT)
Tanana_WSE <- data.frame(read_csv("../input/TOWNS/Raw/TananaWSE.csv", col_names = TRUE)[,c(1:15)])
Tanana_WSE$time <- as.POSIXct(paste0(Tanana_WSE$Date, Tanana_WSE$Time_min), format = "%m/%d/%Y%H:%M:%S")+10800
attr(Tanana_WSE$time, "tzone") <- "America/Los_Angeles"
Tanana_WSE_df <- Tanana_WSE[,c(16, 4:15)]
colnames(Tanana_WSE_df)[c(2:13)] = c(1:12)Load discharge data from USGS
library(dataRetrieval)
siteNumber <- "15515500"
parameterCd <- "00060"
convMetric <- 0.0283168466
Tanana_USGS_Q <- readNWISuv(site = siteNumber, parameterCd = parameterCd,startDate = as.Date(min(Tanana_WSE_df$time)),
endDate = as.Date(max(Tanana_WSE_df$time)+86400))
Tanana_USGS_Q_df <- data.frame(time = Tanana_USGS_Q$dateTime, Q = Tanana_USGS_Q$X_00060_00000*convMetric)Combine both data sets (discharge and WSE)
library(dplyr)
library(tidyr)
library(zoo)
#turn WSE values of zero to NA, interpolate WSE to get to 15 minute resolution instead of 30 minute resolution.
Tanana_trimmed <- Tanana_WSE_df %>% gather(key = "ID", value = "WSE", -time) %>%
mutate(WSE = na_if(WSE, 0)) %>% spread(value = WSE, key = ID) %>% drop_na()
#filter discharge data to match dates
Tanana_long <- Tanana_USGS_Q_df %>%
filter(time >= min(Tanana_trimmed$time))%>%
filter(time <=max(Tanana_trimmed$time))%>%
left_join(Tanana_trimmed) %>%
gather(key = "ID", value = "WSE", -time, - Q) %>%
mutate(WSE = na.approx(WSE))
Tanana_long$ID <- as.numeric(Tanana_long$ID)
datatable(Tanana_long %>% spread(value = WSE, key = ID), caption = "Tanana")Plot WSE
library(ggplot2)
ggplot(data = Tanana_long, aes(x = time, y = WSE, color = ID, group = ID))+geom_point()+ggtitle("Tanana")
Write and save 15-minute, hourly, 6-hourly, and daily
library(dplyr)
library(tidyr)
Tanana_long %>% group_by(Date = format(time, "%Y-%m-%d"), ID)%>%
summarize(WSE = mean(WSE, na.rm = TRUE))## # A tibble: 396 x 3
## # Groups: Date [33]
## Date ID WSE
## <chr> <dbl> <dbl>
## 1 2015-05-19 1 136.
## 2 2015-05-19 2 135.
## 3 2015-05-19 3 134.
## 4 2015-05-19 4 133.
## 5 2015-05-19 5 132.
## 6 2015-05-19 6 131.
## 7 2015-05-19 7 130.
## 8 2015-05-19 8 128.
## 9 2015-05-19 9 127.
## 10 2015-05-19 10 124.
## # ... with 386 more rowsTanana_WSE_Q_daily <- Tanana_long %>% group_by(Date = format(time, "%Y-%m-%d"), ID) %>%
summarize(WSE = mean(WSE, na.rm = TRUE), Q = mean(Q, na.rm = TRUE))%>% spread(value = WSE, key = ID)
Tanana_WSE_Q_hourly <- Tanana_long %>% group_by(Date = format(time, "%Y-%m-%d %H"), ID) %>%
summarize(WSE = mean(WSE, na.rm = TRUE), Q = mean(Q, na.rm = TRUE))%>% spread(value = WSE, key = ID)
every6hrseq <- seq.POSIXt(min(Tanana_long$time), max(Tanana_long$time), by='6 hour')
Tanana_WSE_Q_6_hrs <- Tanana_long %>% group_by(Date = cut(time, breaks=every6hrseq), ID) %>%
summarize(WSE = mean(WSE), Q = mean(Q))%>% spread(value = WSE, key = ID)
Tanana_WSE_Q_15_min <- Tanana_long %>% group_by(Date = time, ID) %>%
summarize(WSE = mean(WSE), Q = mean(Q))%>% spread(value = WSE, key = ID)
saveRDS(Tanana_WSE_Q_daily, "../input/TOWNS/WSE/Tanana_WSE_Q_daily.rds")
saveRDS(Tanana_WSE_Q_hourly, "../input/TOWNS/WSE/Tanana_WSE_Q_hourly.rds")
saveRDS(Tanana_WSE_Q_6_hrs, "../input/TOWNS/WSE/Tanana_WSE_Q_6_hrs.rds")
saveRDS(Tanana_WSE_Q_15_min, "../input/TOWNS/WSE/Tanana_WSE_Q_15_min.rds")Olentangy
Load raw WSE data
library(readr)
Olentangy_WSE <- t(read_csv("../input/TOWNS/Raw/OlentangyH.csv", col_names = FALSE))[,1:20]
Olentangy_WSE_df <- data.frame(WSE = as.numeric(Olentangy_WSE),
time = rep(c(1:nrow(Olentangy_WSE)), times = ncol(Olentangy_WSE)),
ID = rep(c(1:ncol(Olentangy_WSE)), each = nrow(Olentangy_WSE)))Load discharge data (from both USGS, and Tuozzolo et al., 2019 (https://doi.org/10.1016/j.jhydrol.2019.06.038). Tuozzolo et al., 2019 is interpolated from the USGS record to the resolution of the data at 5 minutes. Both are used to match up the timing, and convert to 15 minute resolution.
library(dataRetrieval)
Olentangy_Q <- t(read_csv("../input/TOWNS/Raw/OlentangyQ.csv", col_names = FALSE))
siteNumber <- "03226800"
parameterCd <- "00060"
convMetric <- 0.0283168466
Olentangy_USGS_Q <- readNWISuv(site = siteNumber, parameterCd = parameterCd, startDate = "2014-12-04", endDate = "2014-12-18")
Olentangy_USGS_Q_df <- data.frame(time = Olentangy_USGS_Q$dateTime, Q = Olentangy_USGS_Q$X_00060_00000*convMetric)
Olentangy_USGS_Q_5min <- approx(as.POSIXct(Olentangy_USGS_Q_df$time),
Olentangy_USGS_Q_df$Q, seq(as.POSIXct("2014-12-04"), as.POSIXct("2014-12-18"), by = 5*60))
offset <- which.max(Olentangy_USGS_Q_5min$y) - which.max(Olentangy_Q)
Olentangy_Q_5min <- data.frame(time = Olentangy_USGS_Q_5min$x[offset:(length(Olentangy_Q)+offset-1)],
Q = Olentangy_USGS_Q_5min$y[offset:(length(Olentangy_Q)+offset-1)],
ID = "gauge")Combine both data sets
library(DT)
Olentangy_data_5min <- Olentangy_WSE_df
Olentangy_data_5min$time <- Olentangy_Q_5min$time
Olentangy_data_5min$Q <- Olentangy_Q_5min$Q
datatable(Olentangy_data_5min)Plot WSE
library(ggplot2)
ggplot(data = Olentangy_data_5min, aes(x = time, y = WSE, color = ID, group = ID))+geom_point()
Write and save 15-minute, hourly, 6-hourly, and daily
library(dplyr)
library(tidyr)
Olentangy_WSE_Q_daily <- Olentangy_data_5min %>% group_by(Date = format(time, "%Y-%m-%d"), ID) %>%
summarize(WSE = mean(WSE), Q = mean(Q))%>% spread(value = WSE, key = ID)
Olentangy_WSE_Q_hourly <- Olentangy_data_5min %>% group_by(Date = format(time, "%Y-%m-%d %H"), ID) %>%
summarize(WSE = mean(WSE), Q = mean(Q))%>% spread(value = WSE, key = ID)
every6hrseq <- seq.POSIXt(min(Olentangy_data_5min$time), max(Olentangy_data_5min$time), by='6 hour')
Olentangy_WSE_Q_6_hrs <- Olentangy_data_5min %>% group_by(Date = cut(time, breaks=every6hrseq), ID) %>%
summarize(WSE = mean(WSE), Q = mean(Q))%>% spread(value = WSE, key = ID)
every15minseq <- seq.POSIXt(min(Olentangy_data_5min$time), max(Olentangy_data_5min$time), by='15 min')
Olentangy_WSE_Q_15_min <- Olentangy_data_5min %>% group_by(Date = cut(time, breaks=every15minseq), ID) %>%
summarize(WSE = mean(WSE), Q = mean(Q))%>% spread(value = WSE, key = ID)
saveRDS(Olentangy_WSE_Q_daily, "../input/TOWNS/WSE/Olentangy_WSE_Q_daily.rds")
saveRDS(Olentangy_WSE_Q_hourly, "../input/TOWNS/WSE/Olentangy_WSE_Q_hourly.rds")
saveRDS(Olentangy_WSE_Q_6_hrs, "../input/TOWNS/WSE/Olentangy_WSE_Q_6_hrs.rds")
saveRDS(Olentangy_WSE_Q_15_min, "../input/TOWNS/WSE/Olentangy_WSE_Q_15_min.rds")Willamette
Load raw WSE data
library(stringr)
Willamette_WSE <- data.frame(read.table("../input/TOWNS/Raw/Willamette2015WSE.txt", fill = TRUE, header = TRUE))
Willamette_WSE$HHMMSS <- str_pad(Willamette_WSE$HHMMSS, 6, pad = "0")
Willamette_WSE$time <- as.POSIXct(paste0(Willamette_WSE$YYYYMMDD, Willamette_WSE$HHMMSS), format = "%Y%m%d%H%M")+10800
attr(Willamette_WSE$time, "tzone") <- "America/Los_Angeles"
Willamette_WSE_df <- Willamette_WSE[,c(24, 3:23)]
colnames(Willamette_WSE_df)[c(2:22)] = c(1:21)Load discharge data from USGS
library(dataRetrieval)
siteNumber <- "14166000"
parameterCd <- "00060"
convMetric <- 0.0283168466
Willamette_USGS_Q <- readNWISuv(site = siteNumber, parameterCd = parameterCd,startDate = as.Date(min(Willamette_WSE_df$time)),
endDate = as.Date(max(Willamette_WSE_df$time)+86400))
Willamette_USGS_Q_df <- data.frame(time = Willamette_USGS_Q$dateTime, Q = Willamette_USGS_Q$X_00060_00000*convMetric)Combine both data sets (discharge and WSE)
library(dplyr)
library(tidyr)
#filter discharge data to match dates
Willamette_Q_filt <- Willamette_USGS_Q_df %>%
filter(time >= min(Willamette_WSE_df$time))%>%
filter(time <=max(Willamette_WSE_df$time))%>%
left_join(Willamette_WSE_df)
datatable(Willamette_Q_filt, caption = "Willamette")Plot WSE
library(ggplot2)
Willamette_long <- Willamette_Q_filt %>% gather(key = "ID", value = "WSE", -time, -Q)
Willamette_long$ID <- as.numeric(Willamette_long$ID)
ggplot(data = Willamette_long, aes(x = time, y = WSE, color = ID, group = ID))+geom_point()+ggtitle("Willamette")
Write and save 15-minute, hourly, 6-hourly, and daily
library(dplyr)
library(tidyr)
Willamette_WSE_Q_daily <- Willamette_long %>% group_by(Date = format(time, "%Y-%m-%d"), ID) %>%
summarize(WSE = mean(WSE, na.rm = TRUE), Q = mean(Q, na.rm = TRUE))%>% spread(value = WSE, key = ID)
Willamette_WSE_Q_hourly <- Willamette_long %>% group_by(Date = format(time, "%Y-%m-%d %H"), ID) %>%
summarize(WSE = mean(WSE, na.rm = TRUE), Q = mean(Q, na.rm = TRUE))%>% spread(value = WSE, key = ID)
every6hrseq <- seq.POSIXt(min(Willamette_long$time), max(Willamette_long$time), by='6 hour')
Willamette_WSE_Q_6_hrs <- Willamette_long %>% group_by(Date = cut(time, breaks=every6hrseq), ID) %>%
summarize(WSE = mean(WSE), Q = mean(Q))%>% spread(value = WSE, key = ID)
Willamette_WSE_Q_15_min <- Willamette_long %>% group_by(Date = time, ID) %>%
summarize(WSE = mean(WSE), Q = mean(Q))%>% spread(value = WSE, key = ID)
saveRDS(Willamette_WSE_Q_daily, "../input/TOWNS/WSE/Willamette_WSE_Q_daily.rds")
saveRDS(Willamette_WSE_Q_hourly, "../input/TOWNS/WSE/Willamette_WSE_Q_hourly.rds")
saveRDS(Willamette_WSE_Q_6_hrs, "../input/TOWNS/WSE/Willamette_WSE_Q_6_hrs.rds")
saveRDS(Willamette_WSE_Q_15_min, "../input/TOWNS/WSE/Willamette_WSE_Q_15_min.rds")North Saskatchewan
Load raw WSE data
library(readr)
North_Sask_WSE <- data.frame(read_csv("../input/TOWNS/Raw/Sask_WSE.csv"))
North_Sask_WSE$time <- as.POSIXct(North_Sask_WSE$Time, format = "%m/%d/%Y %H:%M")+3600
attr(North_Sask_WSE$time, "tzone") <- "America/Chicago"
North_Sask_WSE_df <- North_Sask_WSE[,c(11, 2:10)]
colnames(North_Sask_WSE_df)[c(2:10)] = c(1:9)Load discharge data from Water Survey of Canada
library(tidyhydat)
stationNumber <- "05GG001"
#download_hydat()
#Assumes download_hydat() has been run
North_Sask_Q <- hy_daily_flows(station_number = stationNumber, start_date = as.Date(min(North_Sask_WSE_df$time)),
end_date = as.Date(max(North_Sask_WSE_df$time)+86400))
North_Sask_Q_df <- data.frame(time = North_Sask_Q$Date, Q = North_Sask_Q$Value)Combine both data sets (discharge and WSE)
library(dplyr)
library(tidyr)
#filter discharge data to match dates
North_Sask_Q_filt <- North_Sask_Q_df %>%
filter(time >= min(North_Sask_WSE_df$time))%>%
filter(time <=max(North_Sask_WSE_df$time))%>% left_join(North_Sask_WSE_df)
datatable(North_Sask_Q_filt, caption = "North_Sask")Plot WSE
library(ggplot2)
North_Sask_long <- North_Sask_Q_filt %>% gather(key = "ID", value = "WSE", -time, -Q)
North_Sask_long$ID <- as.numeric(North_Sask_long$ID)
ggplot(data = North_Sask_long, aes(x = time, y = WSE, color = ID, group = ID))+geom_point()+ggtitle("North_Sask")
Write and save 15-minute, hourly, 6-hourly, and daily
library(dplyr)
library(tidyr)
North_Sask_WSE_Q_daily <- North_Sask_long %>% group_by(Date = format(time, "%Y-%m-%d"), ID) %>%
summarize(WSE = mean(WSE, na.rm = TRUE), Q = mean(Q, na.rm = TRUE))%>% spread(value = WSE, key = ID)
North_Sask_WSE_Q_hourly <- North_Sask_long %>% group_by(Date = format(time, "%Y-%m-%d %H"), ID) %>%
summarize(WSE = mean(WSE, na.rm = TRUE), Q = mean(Q, na.rm = TRUE))%>% spread(value = WSE, key = ID)
every6hrseq <- seq.POSIXt(min(North_Sask_long$time), max(North_Sask_long$time), by='6 hour')
North_Sask_WSE_Q_6_hrs <- North_Sask_long %>% group_by(Date = cut(time, breaks=every6hrseq), ID) %>%
summarize(WSE = mean(WSE), Q = mean(Q))%>% spread(value = WSE, key = ID)
North_Sask_WSE_Q_15_min <- North_Sask_long %>% group_by(Date = time, ID) %>%
summarize(WSE = mean(WSE), Q = mean(Q))%>% spread(value = WSE, key = ID)
saveRDS(North_Sask_WSE_Q_daily, "../input/TOWNS/WSE/North_Sask_WSE_Q_daily.rds")
saveRDS(North_Sask_WSE_Q_hourly, "../input/TOWNS/WSE/North_Sask_WSE_Q_hourly.rds")
saveRDS(North_Sask_WSE_Q_6_hrs, "../input/TOWNS/WSE/North_Sask_WSE_Q_6_hrs.rds")
saveRDS(North_Sask_WSE_Q_15_min, "../input/TOWNS/WSE/North_Sask_WSE_Q_15_min.rds")Sacramento
Load raw WSE data
library(readr)
library(dplyr)
library(tidyr)
library(DT)
LowSac_vp1 <- read_csv("../input/TOWNS/Raw/LowSac_PT_vp1.csv")
LowSac_vp2 <- read_csv("../input/TOWNS/Raw/LowSac_PT_vp2.csv")
UpSac_vp1 <- read_csv("../input/TOWNS/Raw/UpSac_PT_vp1.csv")
UpSac_vp2 <- read_csv("../input/TOWNS/Raw/UpSac_PT_vp2.csv")
Sacramento_combined <- rbind(LowSac_vp1, LowSac_vp2, UpSac_vp1, UpSac_vp2)
#remove flagged WSE values (pre-install, post-removal, shallow or dry)
Sacramento_combined <- Sacramento_combined[is.na(Sacramento_combined$flag) == "TRUE",]
#check that IDs are descending in terms of WSE
Sacramento_mean <- Sacramento_combined %>% group_by(t_id) %>%
summarize(WSE = mean(wse, na.rm = TRUE))
Sacramento_mean <- Sacramento_mean[order(Sacramento_mean$WSE),]
Sacramento_mean$ID <- order(Sacramento_mean$WSE)
#Rename PTs so that they are in order
Sacramento_renamed <- merge(Sacramento_combined, Sacramento_mean, by = "t_id")
#Split the river into two reaches (upper and lower)
Sacramento_R1 <- Sacramento_renamed %>% filter(ID <= 10)
Sacramento_R2 <- Sacramento_renamed %>% filter(ID <= 16) %>% filter(ID > 10)
Sacramento_R3 <- Sacramento_renamed %>% filter(ID > 16)
Sacramento_R1_WSE_df <- data.frame(WSE = Sacramento_R1$wse,
time = as.POSIXct(Sacramento_R1$date_time, format = "%m/%d/%Y %H:%M", tz = "MST"),
ID = Sacramento_R1$ID)
Sacramento_R2_WSE_df <- data.frame(WSE = Sacramento_R2$wse,
time = as.POSIXct(Sacramento_R2$date_time, format = "%m/%d/%Y %H:%M", tz = "MST"),
ID = Sacramento_R2$ID)
Sacramento_R3_WSE_df <- data.frame(WSE = Sacramento_R3$wse,
time = as.POSIXct(Sacramento_R3$date_time, format = "%m/%d/%Y %H:%M", tz = "MST"),
ID = Sacramento_R3$ID)Load discharge data from USGS
library(dataRetrieval)
siteNumber <- "11389500"
parameterCd <- "00060"
convMetric <- 0.0283168466
Sacramento_R1_USGS_Q <- readNWISuv(site = siteNumber, parameterCd = parameterCd,
startDate = as.Date(min(Sacramento_R1_WSE_df$time)),
endDate = as.Date(max(Sacramento_R1_WSE_df$time)+86400))
Sacramento_R2_USGS_Q <- readNWISuv(site = siteNumber, parameterCd = parameterCd,
startDate = as.Date(min(Sacramento_R2_WSE_df$time)),
endDate = as.Date(max(Sacramento_R2_WSE_df$time)+86400))
Sacramento_R3_USGS_Q <- readNWISuv(site = siteNumber, parameterCd = parameterCd,
startDate = as.Date(min(Sacramento_R3_WSE_df$time)),
endDate = as.Date(max(Sacramento_R3_WSE_df$time)+86400))
Sacramento_R1_USGS_Q_df <- data.frame(time = as.POSIXct(Sacramento_R1_USGS_Q$dateTime),
Q = Sacramento_R1_USGS_Q$X_00060_00000*convMetric)
Sacramento_R2_USGS_Q_df <- data.frame(time = as.POSIXct(Sacramento_R2_USGS_Q$dateTime),
Q = Sacramento_R2_USGS_Q$X_00060_00000*convMetric)
Sacramento_R3_USGS_Q_df <- data.frame(time = as.POSIXct(Sacramento_R3_USGS_Q$dateTime),
Q = Sacramento_R3_USGS_Q$X_00060_00000*convMetric)Combine both data sets (discharge and WSE)
library(dplyr)
library(tidyr)
#filter discharge data to match dates
Sacramento_R1_WSE_spread <- Sacramento_R1_WSE_df %>% spread(value = WSE, key = ID) %>% drop_na()
Sacramento_R1_Q_filt <- Sacramento_R1_USGS_Q_df %>%
filter(time >= min(Sacramento_R1_WSE_spread$time))%>%
filter(time <=max(Sacramento_R1_WSE_spread$time))%>%
left_join(Sacramento_R1_WSE_spread)
Sacramento_R2_WSE_spread <- Sacramento_R2_WSE_df %>% spread(value = WSE, key = ID) %>% drop_na()
Sacramento_R2_Q_filt <- Sacramento_R2_USGS_Q_df %>%
filter(time >= min(Sacramento_R2_WSE_spread$time))%>%
filter(time <=max(Sacramento_R2_WSE_spread$time))%>%
left_join(Sacramento_R2_WSE_spread)
Sacramento_R3_WSE_spread <- Sacramento_R3_WSE_df %>% spread(value = WSE, key = ID) %>% drop_na()
Sacramento_R3_Q_filt <- Sacramento_R3_USGS_Q_df %>%
filter(time >= min(Sacramento_R3_WSE_spread$time))%>%
filter(time <=max(Sacramento_R3_WSE_spread$time))%>%
left_join(Sacramento_R3_WSE_spread)
datatable(Sacramento_R1_Q_filt, caption = "Sacramento Reach One")datatable(Sacramento_R2_Q_filt, caption = "Sacramento Reach Two")datatable(Sacramento_R3_Q_filt, caption = "Sacramento Reach Three")Plot WSE
library(ggplot2)
Sacramento_R1_long <- Sacramento_R1_Q_filt %>% gather(key = "ID", value = "WSE", -time, -Q)
Sacramento_R1_long$ID <- as.numeric(Sacramento_R1_long$ID)
ggplot(data = Sacramento_R1_long, aes(x = time, y = WSE, color = ID, group = ID))+geom_point()+ggtitle("Sacramento Reach One")
Sacramento_R2_long <- Sacramento_R2_Q_filt %>% gather(key = "ID", value = "WSE", -time, -Q)
Sacramento_R2_long$ID <- as.numeric(Sacramento_R2_long$ID)
ggplot(data = Sacramento_R2_long, aes(x = time, y = WSE, color = ID, group = ID))+geom_point()+ggtitle("Sacramento Reach Two")
Sacramento_R3_long <- Sacramento_R3_Q_filt %>% gather(key = "ID", value = "WSE", -time, -Q)
Sacramento_R3_long$ID <- as.numeric(Sacramento_R3_long$ID)
ggplot(data = Sacramento_R3_long, aes(x = time, y = WSE, color = ID, group = ID))+geom_point()+ggtitle("Sacramento Reach Three")
Write and save 15-minute, hourly, 6-hourly, and daily
library(dplyr)
library(tidyr)
#add 30.9942 for corrective WSE
Sacramento_R1_WSE_Q_daily <- Sacramento_R1_long %>% group_by(Date = format(time, "%Y-%m-%d"), ID) %>%
summarize(WSE = mean(WSE, na.rm = TRUE)+30.9942, Q = mean(Q, na.rm = TRUE))%>% spread(value = WSE, key = ID)
Sacramento_R2_WSE_Q_daily <- Sacramento_R2_long %>% group_by(Date = format(time, "%Y-%m-%d"), ID) %>%
summarize(WSE = mean(WSE, na.rm = TRUE)+30.9942, Q = mean(Q, na.rm = TRUE))%>% spread(value = WSE, key = ID)
Sacramento_R3_WSE_Q_daily <- Sacramento_R3_long %>% group_by(Date = format(time, "%Y-%m-%d"), ID) %>%
summarize(WSE = mean(WSE, na.rm = TRUE)+30.9942, Q = mean(Q, na.rm = TRUE))%>% spread(value = WSE, key = ID)
Sacramento_R1_WSE_Q_hourly <- Sacramento_R1_long %>% group_by(Date = format(time, "%Y-%m-%d %H"), ID) %>%
summarize(WSE = mean(WSE, na.rm = TRUE)+30.9942, Q = mean(Q, na.rm = TRUE))%>% spread(value = WSE, key = ID)
Sacramento_R2_WSE_Q_hourly<- Sacramento_R2_long %>% group_by(Date = format(time, "%Y-%m-%d %H"), ID) %>%
summarize(WSE = mean(WSE, na.rm = TRUE)+30.9942, Q = mean(Q, na.rm = TRUE))%>% spread(value = WSE, key = ID)
Sacramento_R3_WSE_Q_hourly <- Sacramento_R3_long %>% group_by(Date = format(time, "%Y-%m-%d %H"), ID) %>%
summarize(WSE = mean(WSE, na.rm = TRUE)+30.9942, Q = mean(Q, na.rm = TRUE))%>% spread(value = WSE, key = ID)
R1_every6hrseq <- seq.POSIXt(min(Sacramento_R1_long$time), max(Sacramento_R1_long$time), by='6 hour')
R2_every6hrseq <- seq.POSIXt(min(Sacramento_R2_long$time), max(Sacramento_R2_long$time), by='6 hour')
R3_every6hrseq <- seq.POSIXt(min(Sacramento_R3_long$time), max(Sacramento_R3_long$time), by='6 hour')
Sacramento_R1_WSE_Q_6_hrs <- Sacramento_R1_long %>% group_by(Date = cut(time, breaks=R1_every6hrseq), ID) %>%
summarize(WSE = mean(WSE)+30.9942, Q = mean(Q))%>% spread(value = WSE, key = ID)
Sacramento_R2_WSE_Q_6_hrs <- Sacramento_R2_long %>% group_by(Date = cut(time, breaks=R2_every6hrseq), ID) %>%
summarize(WSE = mean(WSE)+30.9942, Q = mean(Q))%>% spread(value = WSE, key = ID)
Sacramento_R3_WSE_Q_6_hrs <- Sacramento_R3_long %>% group_by(Date = cut(time, breaks=R3_every6hrseq), ID) %>%
summarize(WSE = mean(WSE)+30.9942, Q = mean(Q))%>% spread(value = WSE, key = ID)
Sacramento_R1_WSE_Q_15_min <- Sacramento_R1_long %>% group_by(Date = time, ID) %>%
summarize(WSE = mean(WSE)+30.9942, Q = mean(Q))%>% spread(value = WSE, key = ID)
Sacramento_R2_WSE_Q_15_min <- Sacramento_R2_long %>% group_by(Date = time, ID) %>%
summarize(WSE = mean(WSE)+30.9942, Q = mean(Q))%>% spread(value = WSE, key = ID)
Sacramento_R3_WSE_Q_15_min <- Sacramento_R3_long %>% group_by(Date = time, ID) %>%
summarize(WSE = mean(WSE)+30.9942, Q = mean(Q))%>% spread(value = WSE, key = ID)
saveRDS(Sacramento_R1_WSE_Q_daily, "../input/TOWNS/WSE/Sacramento_R1_WSE_Q_daily.rds")
saveRDS(Sacramento_R1_WSE_Q_hourly, "../input/TOWNS/WSE/Sacramento_R1_WSE_Q_hourly.rds")
saveRDS(Sacramento_R1_WSE_Q_6_hrs, "../input/TOWNS/WSE/Sacramento_R1_WSE_Q_6_hrs.rds")
saveRDS(Sacramento_R1_WSE_Q_15_min, "../input/TOWNS/WSE/Sacramento_R1_WSE_Q_15_min.rds")
saveRDS(Sacramento_R2_WSE_Q_daily, "../input/TOWNS/WSE/Sacramento_R2_WSE_Q_daily.rds")
saveRDS(Sacramento_R2_WSE_Q_hourly, "../input/TOWNS/WSE/Sacramento_R2_WSE_Q_hourly.rds")
saveRDS(Sacramento_R2_WSE_Q_6_hrs, "../input/TOWNS/WSE/Sacramento_R2_WSE_Q_6_hrs.rds")
saveRDS(Sacramento_R2_WSE_Q_15_min, "../input/TOWNS/WSE/Sacramento_R2_WSE_Q_15_min.rds")
saveRDS(Sacramento_R3_WSE_Q_daily, "../input/TOWNS/WSE/Sacramento_R3_WSE_Q_daily.rds")
saveRDS(Sacramento_R3_WSE_Q_hourly, "../input/TOWNS/WSE/Sacramento_R3_WSE_Q_hourly.rds")
saveRDS(Sacramento_R3_WSE_Q_6_hrs, "../input/TOWNS/WSE/Sacramento_R3_WSE_Q_6_hrs.rds")
saveRDS(Sacramento_R3_WSE_Q_15_min, "../input/TOWNS/WSE/Sacramento_R3_WSE_Q_15_min.rds")